TiCC Colloquium: Matthew Crocker

Abstract

Cognitive models of language comprehension seek
to map utterances to meanings in a manner that is informed by, and also
explains, online behavioural and neurophysiological indices such as reading
times and event-related potentials. In this talk I will outline our ongoing
efforts efforts to develop such a neurocomputational model. I will begin by
introducing the distributed situation-space (DSS) approach to representing
meaning that we adopt. The framework not only supports the representation of
arbitrarily complex compositional logical forms, but also encodes their
likelihood in the world, and supports knowledge-driven inference. Further, as
shown in Venhuizen, Crocker & Brouwer (2018), DSS representations supports
the computation of a 'meaning-centric' notion of surprisal, on a word-by-word
basis. I will then show how training a recurrent network to recover such DSS
representations for input utterance – in which the training corpus reflects the
linguistic frequency distribution – results in a model where the surprisal of a
word reflects both the likelihood of the meaning it induces and linguistic
expectancy of that word. I will briefly outline ongoing work to integrate this
model into the neurocomputational model of Brouwer, Crocker, Venhuizen &
Hoeks (2017), which identifies a clear linking hypothesis to the N400 (lexical
retrieval) and P600 (semantic integration) ERP components in the EEG signal. A
key prediction of the model is that semantic integration difficulty should (a)
result in increased surprisal, and (b) be manifest as an increased P600
amplitude. I will then present the findings of a recents ERP experiment we
conducted to test this prediction (Delogu, Brouwer & Crocker, 2018).